Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer
Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/9303676 |
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| _version_ | 1832555935341477888 |
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| author | Dalian Yang Jingjing Miao Fanyu Zhang Jie Tao Guangbin Wang Yiping Shen |
| author_facet | Dalian Yang Jingjing Miao Fanyu Zhang Jie Tao Guangbin Wang Yiping Shen |
| author_sort | Dalian Yang |
| collection | DOAJ |
| description | Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters are difficult to select. To address these limitations, an escape mechanism and adaptive convergence conditions were introduced to the ALO algorithm. As a result, the EALO method was proposed and has been applied to the more accurate selection of SVM model parameters. To assess the model, the vibration acceleration signals of normal, inner ring fault, outer ring fault, and ball fault bearings were collected at different rotation speeds (1500 r/min, 1800 r/min, 2100 r/min, and 2400 r/min). The vibration signals were decomposed using the variational mode decomposition (VMD) method. The features were extracted through the kernel function to fuse the energy value of each VMD component. In these experiments, the two most important parameters for the support vector machine—the Gaussian kernel parameter σ and the penalty factor C—were optimized using the EALO algorithm, ALO algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. The performance of these four methods to optimize the two parameters was then compared and analyzed, with the EALO method having the best performance. The recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used. |
| format | Article |
| id | doaj-art-9a4e89cc0642418ea530034928f49090 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-9a4e89cc0642418ea530034928f490902025-02-03T05:46:48ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/93036769303676Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion OptimizerDalian Yang0Jingjing Miao1Fanyu Zhang2Jie Tao3Guangbin Wang4Yiping Shen5Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaHunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaHunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaKey Laboratory of Knowledge Processing and Networked Manufacturing, Hunan University of Science and Technology, Xiangtan 411201, ChinaHunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaHunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, ChinaBearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters are difficult to select. To address these limitations, an escape mechanism and adaptive convergence conditions were introduced to the ALO algorithm. As a result, the EALO method was proposed and has been applied to the more accurate selection of SVM model parameters. To assess the model, the vibration acceleration signals of normal, inner ring fault, outer ring fault, and ball fault bearings were collected at different rotation speeds (1500 r/min, 1800 r/min, 2100 r/min, and 2400 r/min). The vibration signals were decomposed using the variational mode decomposition (VMD) method. The features were extracted through the kernel function to fuse the energy value of each VMD component. In these experiments, the two most important parameters for the support vector machine—the Gaussian kernel parameter σ and the penalty factor C—were optimized using the EALO algorithm, ALO algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. The performance of these four methods to optimize the two parameters was then compared and analyzed, with the EALO method having the best performance. The recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used.http://dx.doi.org/10.1155/2019/9303676 |
| spellingShingle | Dalian Yang Jingjing Miao Fanyu Zhang Jie Tao Guangbin Wang Yiping Shen Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer Shock and Vibration |
| title | Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer |
| title_full | Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer |
| title_fullStr | Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer |
| title_full_unstemmed | Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer |
| title_short | Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer |
| title_sort | bearing fault diagnosis using a support vector machine optimized by an improved ant lion optimizer |
| url | http://dx.doi.org/10.1155/2019/9303676 |
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